Personalizing Ride Experience Based on Contextual Ride Usage Data

ABSTRACT

A transport system can manage an on-demand transportation service to connect available vehicles with users, and can compile ride history data for each user. The ride history data can indicate the contextual usage of the on-demand transportation service by the user. Based on the ride history data, the transport system can determine demographic and personal interest information of the respective user. The transport system may then personalize one or more ride characteristics of any ride requested by the user based on the demographic and personal interest information determined from the ride history of the user.

BACKGROUND

Content targeting has evolved significantly in terms of precision andubiquity. For example, advertisers could perform a cursory analysis of acertain population to determine a general set of interests, and connecta product or service to those interests. Thus, a billboard advertisementat a sporting venue may involve snacks or beverages that sportingenthusiasts may typically enjoy while experiencing a match or game.Advertisers have also used applied psychology and behaviorism toconstruct advertisements that appeal to certain basic emotions oftargeted consumers, such as love, hate, and fear. The extremeeffectiveness of such techniques has led to pervasion of consumeradvertising throughout nearly all cultures and societies in the world.

With the onset of the Internet, online advertising quickly evolvedthrough a series of phases, such as early banner advertisements onwebsites, relentless pop-up advertisements over preferred content,pay-per-click revenue models, and eventually to more standardizedHyperText Markup Language (HTML) constructs providing designated blocksfor advertisement images, texts, animations, videos, and other content.Concurrently, app developers have utilized mobile advertising companiesto insert advertisements through mobile device operating systems and webbrowsers. In addition, native or disguised advertising—which matches theform and function of the platform upon which it appears—has become moreand more ubiquitous, purportedly offering a less intrusive user contentbrowsing experience.

Browsers, search engines, and social media platforms continue toaccumulate massive amounts of data for each user through browsinghistory, text parsing, associations with other users, and feedbackinformation, in order to provide more narrowly targeted advertisementsto users. These entities seek to determine user interests on anindividualized and/or clustered level in order to provide narrowlytargeted advertisements for those users. From a content-providerstandpoint, advertisements have typically been used as a source ofrevenue and/or as an alternative revenue source to counter contentsubscriptions.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure herein is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings in which likereference numerals refer to similar elements, and in which:

FIG. 1 is a block diagram illustrating an example transport facilitationsystem in communication with riders and vehicles, in accordance withexamples described herein;

FIG. 2 is a block diagram illustrating an example ride experiencepersonalization system, according to examples described herein;

FIG. 3 is a block diagram illustrating an example rider device incommunication with a transport system, as described herein;

FIG. 4 is a flow chart describing an example method of personalizingride experience based on contextual ride usage data, according toexamples described herein;

FIG. 5 is a lower level flow chart describing an example method ofpersonalizing ride experience based on contextual ride usage data,according to examples described herein; and

FIG. 6 is a block diagram illustrating a computer system for a backenddatacenter upon which example transport systems described herein may beimplemented.

DETAILED DESCRIPTION

A transport facilitation system is discussed herein that manages anon-demand transportation arrangement service linking available driversand/or autonomous vehicles (AVs) with requesting riders throughout agiven region (e.g., a metroplex such as the San Francisco Bay Area). Indoing so, the transport facilitation system (or “transport system”) canreceive user requests for transportation from requesting users via adesignated rider application executing on the users' computing devices.Based on a determined or inputted pick-up location, the transport systemcan identify a number of proximate available vehicles and transmit atransport invitation to a driver device of the proximate availablevehicles or to a communication interface of an AV to service the pick-uprequest.

In determining a most optimal driver or AV to service a given pick-uprequest, the transport system can identify a plurality of candidatedrivers and/or AVs to service the pick-up request based on a pick-uplocation indicated in the pick-up request. As provided herein, an“optimal” driver or AV corresponds to a driver or AV, from a candidateset of vehicles, that has been determined to be most suitable toservicing a particular pick-up request based on one or more factors. Thefactor(s) can comprise the vehicle being closest to the pick-uplocation, having a lowest estimated time of arrival (ETA) to the pick-uplocation, an estimated collective value generated by the driver in beingselected as compared to other drivers or AVs, and other factors. In someaspects, the transport system can identify a set of candidate vehicles(e.g., twenty or thirty vehicles within a certain proximity of thepick-up location), and select a most optimal vehicle (e.g., a closestvehicle to the pick-up location, a vehicle with the shortest estimatedtravel time from the pick-up location, a vehicle traveling to a locationwithin a specified distance or specified travel time to the destinationlocation, etc.) from the candidate vehicles to service the pick-uprequest based on any of the above factors. Example backend transportsystems described herein can comprise a network-based transportmanagement system that facilitates on-demand transportation arrangementservices, such as those provided by Uber Technologies, Inc., of SanFrancisco, Calif.

According to examples described herein, a transport system can compileride history data for each respective user of the on-demandtransportation service, the ride history data can indicate contextualusage of the on-demand transportation service by the respective user.Based on the ride history data, the transport system can determinedemographic and personal interest information of each user, andpersonalize a number of ride characteristics for requested rides basedon the demographic and personal interest information of the user. Insome aspects, the transport system can do so by providing personalizedcontent to the user on a display screen of the available vehicle or adisplay screen of a user device of the respective user (e.g., a smartphone or tablet computing device).

The ride history data can comprise spatial and temporal datacorresponding to the user's usage of the on-demand ride service, such astime of day usage, day of week usage, drop-off and pick-up locationcharacteristics, selected ride service types, preferred vehicle typesand characteristics, driver or service ratings information provided bythe user, and the like. For example, destinations inputted by the usercan correspond to interests in a variety of services, hobbies, products,activities, professions, brands, and the like. Time of day and/or day ofweek information can provide further context to determining the user'sinterests and characteristics, such as the user's profession or mainhobbies. Likewise, user demographics, such as age, gender or sex, incomelevel, whether the user is a parent, etc., can be determined based onthe user's contextual usage of the on-demand ride service. Along theselines, the transport system can combine demographic data with thedetermined interests to bolster the user's ride experience whenutilizing the on-demand transportation service.

In certain implementations, when the user/rider is on-trip to adestination, the transport system can provide personalized content forthe user, either via on-board display screens in the vehicle, or via adisplay screen of the rider's computing device (e.g., through anexecuting rider application). The personalized content can comprisecontent corresponding to any number of personalized user accounts of therider, such as news articles, social media content, gaming content,targeted advertising content, and the like. In further examples, thetransport system can discount a cost of the ride based, at least inpart, on the amount of consumed advertising content, and/or a level ofinteraction with the advertising content by the rider. As providedherein, the advertising content may be targeted for the rider based onthe various information gleaned from the rider's contextual usage of theon-demand transportation service. In some aspects, the discounting ofthe ride can be calculated based on a direct correlation with thebackend revenue generated from the rider's ad consumption.

In further aspects, the transport system can provide riders and driverswith designated applications, executable on rider and driver devicesrespectively, that enable riders to make transport requests and driversto accept transport service invitations. In certain examples, the riderapplication can include a selectable feature enabling the rider toselect from viewing advertising content or suppressing advertisingcontent during the ride. As described herein, the advertising contentcan be provided to the rider via the rider application executing on therider's device, or can be displayed on display screens in the passengerinterior of the vehicle (e.g., accessible in the back seats of thedriver's vehicle or a servicing self-driving vehicle). Along theselines, the transport system can monitor a level of interaction with theadvertising content during the ride, and optionally discount the ridebased on the monitored level of interaction with the advertisingcontent. In addition, certain features of the rider's device can beutilized through the rider application in order to determine demographicinformation of the rider. For example, the transport system may accessaccelerometer data from the rider's device to construct a gait patternof the rider as the rider walks. The gait pattern can indicate, forexample, the height, weight, and body type of the rider.

In various implementations, the personal interests of the rider—asdetermined from the ride usage history—can further be utilized todetermine preferred routes to a destination (e.g., a scenic route versusa direct route). In further implementations, the transport system cananalyze the ride history data for the users in order to cluster theusers of based on common demographics or common personal interests. Foreach specified cluster, the transport system can establish a set ofinterest parameters that provides each user in the specified clusterwith ride experience characteristics (e.g., displayed content) specificto the cluster. For example, the set of interest parameters can specifycontent to be displayed to users in the specified cluster, one or morevehicle types to be selected for the users in the specified cluster(e.g., a standard vehicle, a luxury vehicle, a high capacity vehicle, asport utility vehicle, a specific vehicle brand, or a self-drivingvehicle), or a preferred ride service type to be selected for the usersin the specified cluster (e.g., a carpooling service, a standardride-sharing service, a professional driver service, a black carservice, a luxury ride service, a high capacity vehicle service, or aself-driving vehicle service). In one example, the transport system canexecute a machine learning model to classify each user into one or moreclusters and customize the plurality of ride characteristics of the ridebased the set of parameters for each of the one or more clusters. Alongthese lines, the transport system can cluster the users, based on commondemographics and/or common personal interests, by representing each ofthe users as a vector comprising a series of values indicative of eachcluster in which the user is classified.

Among other benefits, the examples described herein achieve a technicaleffect of improving the ride experience of users of an on-demandtransportation service. In doing so, the transport system disclosedherein can utilize contextual usage data of the transportation servicein order to determine user information, such as demographic and personalinterest information. In many respects, this information can be utilizedin order to provide the user with personalized content (e.g., either viathe user's computing device or interior displays of the matched vehicle,such as a self-driving vehicle). In one example, the transport systemcan monitor the user's interaction and/or consumption of thepersonalized content in order discount a cost of a serviced ride orprovide a user credit for a future ride.

As used herein, a computing device refers to devices corresponding todesktop computers, cellular devices or smartphones, personal digitalassistants (PDAs), laptop computers, tablet devices, virtual reality(VR) and/or augmented reality (AR) devices, wearable computing devices,television (IP Television), etc., that can provide network connectivityand processing resources for communicating with the system over anetwork. A computing device can also correspond to custom hardware,in-vehicle devices, or on-board computers, etc. The computing device canalso operate a designated application configured to communicate with thenetwork service.

One or more examples described herein provide that methods, techniques,and actions performed by a computing device are performedprogrammatically, or as a computer-implemented method. Programmatically,as used herein, means through the use of code or computer-executableinstructions. These instructions can be stored in one or more memoryresources of the computing device. A programmatically performed step mayor may not be automatic.

One or more examples described herein can be implemented usingprogrammatic modules, engines, or components. A programmatic module,engine, or component can include a program, a sub-routine, a portion ofa program, or a software component or a hardware component capable ofperforming one or more stated tasks or functions. As used herein, amodule or component can exist on a hardware component independently ofother modules or components. Alternatively, a module or component can bea shared element or process of other modules, programs or machines.

Some examples described herein can generally require the use ofcomputing devices, including processing and memory resources. Forexample, one or more examples described herein may be implemented, inwhole or in part, on computing devices such as servers, desktopcomputers, cellular or smartphones, personal digital assistants (e.g.,PDAs), laptop computers, virtual reality (VR) or augmented reality (AR)computers, network equipment (e.g., routers) and tablet devices. Memory,processing, and network resources may all be used in connection with theestablishment, use, or performance of any example described herein(including with the performance of any method or with the implementationof any system).

Furthermore, one or more examples described herein may be implementedthrough the use of instructions that are executable by one or moreprocessors. These instructions may be carried on a computer-readablemedium. Machines shown or described with figures below provide examplesof processing resources and computer-readable mediums on whichinstructions for implementing examples disclosed herein can be carriedand/or executed. In particular, the numerous machines shown withexamples of the invention include processors and various forms of memoryfor holding data and instructions. Examples of computer-readable mediumsinclude permanent memory storage devices, such as hard drives onpersonal computers or servers. Other examples of computer storagemediums include portable storage units, such as CD or DVD units, flashmemory (such as those carried on smartphones, multifunctional devices ortablets), and magnetic memory. Computers, terminals, network enableddevices (e.g., mobile devices, such as cell phones) are all examples ofmachines and devices that utilize processors, memory, and instructionsstored on computer-readable mediums. Additionally, examples may beimplemented in the form of computer-programs, or a computer usablecarrier medium capable of carrying such a program.

As provided herein, the terms “autonomous vehicle” (AV) or “self-drivingvehicle” (SDV) may be used interchangeably to describe any vehicleoperating in a state of autonomous control with respect to acceleration,steering, and braking. Different levels of autonomy may exist withrespect to AVs and SDVs. For example, some vehicles may enableautonomous control in limited scenarios, such as on highways. Moreadvanced AVs and SDVs can operate in a variety of traffic environmentswithout any human assistance. Accordingly, an “AV control system” canprocess sensor data from the AV or SDV's sensor array, and modulateacceleration, steering, and braking inputs to safely drive the AV or SDValong a given route.

System Description

FIG. 1 is a block diagram illustrating an example transport facilitationsystem in communication with user and driver devices, in accordance withexamples described herein. The transport facilitation system 100 canmanage a transportation arrangement service that connects requestingusers or riders 174 with drivers 184 that are available to service theusers' 174 pick-up requests 171. The transportation arrangement servicecan provide a platform that enables ride sharing services betweenrequesting users 174 and available drivers 184 by way of a riderapplication 175 executing on the rider devices 170, and a driverapplication 185 executing on the driver devices 180. As used herein, arider device 170 and a driver device 180 can comprise computing deviceswith functionality to execute a designated application corresponding tothe on-demand transportation arrangement service managed by thetransport facilitation system 100. In many examples, the rider device170 and the driver device 180 can comprise mobile computing devices,such as smartphones, tablet computers, VR or AR headsets, on-boardcomputing systems of vehicles, personal computers, laptops, wearablecomputing devices, and the like. Example transportation arrangementservices implementing a ride sharing platform include those provided byUBER Technologies, Inc. of San Francisco, Calif.

In further examples, the transport system 100 can connect requestingusers 174 with available self-driving vehicles (“SDVs””) 194 operatingthroughout the given region. As such, when a requesting user 174 submitsa pick-up request 171 via the executing rider application 175, thetransport system 100 may select a proximate available SDV 194 to servicethe pick-up request 171. As provided herein, the SDV 194 and/or drivervehicle may include interior display screens (e.g., includingtouch-sensitive capabilities) that enable the requesting user 174 toperceive and interact with display content while the requesting user 174is on-trip to a given destination 172.

The transport system 100 can include a rider interface 125 tocommunicate with rider devices 170 over one or more networks 160 via therider application 175. According to examples, a requesting user 174wishing to utilize the transportation arrangement service can launch therider application 175 and transmit a pick-up request 171 over thenetwork 160 to the transport system 100. In certain implementations, therequesting rider 174 can view multiple different service types managedby the transport system 100, such as ride-pooling, a standard ride shareservice type, a luxury vehicle service type, a high-capacity van orlarge vehicle service type, a professional driver service (e.g., wherethe driver is certified), a self-driving vehicle transport service,other specialized ride services, and the like. In some examples, thetransport system 100 can utilize the vehicle locations 113 (e.g., via aGPS module of the driver device 180 or the SDV 194) to provide the riderdevices 170 with ETA data 164 of proximate drivers 184 and SDVs 194 foreach respective service type. For example, the rider application 175 caninclude a service type selection filter that enables the user 174 toview information corresponding to each respective service type, such asETA data 164 for a selected service type.

In some examples, the pick-up request 171 can include a pick-up locationwithin a given region (e.g., a metropolitan area managed by one or moredatacenters corresponding to the transport system 100) in which amatched driver 184 or SDV 194 is to rendezvous with the requesting user174. The pick-up location can be inputted by the user by setting alocation pin on a user interface of the rider app 175, or can bedetermined by a current location 173 of the requesting user 174 (e.g.,utilizing location-based resources of the rider device 170).Additionally, the requesting user 174 can further input a destination172 during or after submitting the pick-up request 171.

According to examples described herein, the user interface 152 of therider application 175 can query the rider 174 for a destination, orotherwise provide input features on the user interface 152 to receivedata indicating a desired destination location 172. The user interface152 can also enable the requesting user 174 to indicate a preferred rideservice type and/or any specialized requests for the ride. As providedherein, the ride service types can comprise one or more carpoolingservice types, a standard ride-sharing service type (e.g., a normal carand operator), premium ride-sharing service types (e.g., black car,luxury vehicle, high capacity, luxury high-capacity, professional driverservices), self-driving vehicle services, and can further include anynumber of specialized service request features, such as disabilityvehicle features and/or assistance, baby or toddler seat, bike rack,pick-up truck, roof racks, audio and/or video configurations, Wi-Fiaccess requests, and the like.

In accordance with some examples, the transport system 100 can include adatabase 140 storing rider profiles 142, driver profiles, and SDVprofiles (not shown). The driver profile for a particular driver caninclude identifying information, such as vehicle information (e.g.,vehicle model, year, license plate number, and color), the driver'soverall rating, qualified service types (e.g., professional driver,certified assistance driver, etc.), experience, earnings, and the like.In variations, the driver profile can further include the driver'spreferences, such as preferred service areas, routes, hours ofoperation, and the like. Likewise, an SDV profile can indicate thevehicle type, capacity, model and year, mileage, current power or fuellevel, and the like. As such the driver and/or SDV profiles may bedynamically updated through location and data pings from the driverdevice 180 and/or SDV 194.

In various implementations, the transport system 100 can include aselection engine 130 to process the pick-up requests 171 in order toultimately select from a pool of drivers 184 and/or self-drivingvehicles 194 operating throughout the given region to service thepick-up requests 171. The transport system 100 can include a vehicleinterface 115 to communicate with the SDVs 194 and the driver devices180. In accordance with various examples, the driver devices 180 and SDVs 194 can transmit their current locations 113 using location-basedresources (e.g., GPS resources). These vehicle locations 113 can beutilized by the selection engine 130 to identify a set of candidatedrivers and/or SDVs (e.g., a set of twenty or thirty closestvehicles)—in relation to the pick-up location—that can service thepick-up request 171. The selection engine 130 can select a most optimaldriver 184 or SDV 194 from the candidate set based on any number offactors. For example, the selection engine 130 can select a driver 184or SDV 194 based on a shortest distance or time to the pick-up location.

Additionally or alternatively, the selection engine 130 can select adriver or SDV 194 based on ride service supply in highly localizedareas. For example, the selection engine 130 may identify that anavailable vehicle is operating in a local area that is short oftransportation supply, and may thus select an alternative vehicle toservice the pick-up request 171. In such examples, the selection engine130 can utilize a mapping engine 135 providing map data 137 and/ortraffic data 139 to determine the route distance and/or route time ofany given driver 184 or SDV 194 in a candidate set to travel to thepick-up location. In certain implementations, the selection engine 130can further utilize profile data 149 from the requesting user's 174rider profile 142 in order to ultimately match the requesting user 174with an available driver 184 or SDV 194. As provided herein, the profiledata 149 from the requesting user's 174 rider profile 142 can indicatethe requesting user's 174 preferences and personal interests.Accordingly, in some examples, the selection engine 130 can furtherreference driver and/or vehicle profiles in the database 140 in order tomatch the requesting user 174 with a driver 184 or SDV 194 that canaccommodate the preferences and personal interests indicated in theprofile data 149 of the requesting user 174.

According to various implementations, the selection engine 130 canperform an optimization over a set of factors in order to match therequesting user 174 with an available driver 184 or SDV 194. Thesefactors can be weighted in a predetermined manner geared towards, forexample, maximizing rider satisfaction. In some aspects, more heavilyweighted factors can comprise wait time for the requesting user 174, orETA to the pick-up location, based on the map data 137 and traffic data139. The optimization factors can further include localizedtransportation supply and other high level valuation metrics thatmaximize the overall value of the on-demand transportation service forthe given region. Still further, as described herein, the optimizationfactors can further include factors corresponding to the demographics,personal interests, and/or preferences of the requesting user 174 asdetermined from ride history data 143 in the user's 174 rider profile142.

Once an optimal driver 184 is selected, the selection engine 130 cangenerate a transport invitation 132 to service the pick-up request 171,and transmit the transport invitation 132 to the optimal driver 184 viathe driver application 185 executing on the optimal driver's computingdevice 180. Upon receiving the transport invitation 132, the optimaldriver 189 can either accept or reject the invitation 132. Rejection ofthe invitation 132 can cause the selection engine 130 to determineanother optimal driver from the candidate set of drivers 184 to servicethe pick-up request 171, or can cause the selection engine 130 todetermine a new set of candidate drivers from which to select anotherdriver. If the optimal driver 189 accepts (e.g., via an acceptanceinput), then the acceptance input 181 can be transmitted back to theselection engine 130, which can generate and transmit a confirmation 134of the optimal driver 189 to the requesting user 174 via the riderapplication 175 executing on the requesting user's 174 computing device170. If the optimal vehicle is an SDV 194, the selection engine cantransmit the transport invitation 132 or an instruction to acommunications interface of the SDV 194. In some aspects, the invitation132 or instruction can include route information that instructs the SDV194 to autonomously drive to the pick-up location in order to rendezvouswith the requesting user 174.

In various examples, the transport system can include a profile manager150 to compile and analyze ride history data 143 for each user 174 ofthe on-demand transportation service. The ride history data 143 canindicate contextual usage of the on-demand transportation service by therequesting user 174. The contextual usage can correspond to pick-up anddrop-off locations, alternate and/or preferred routes, time-of dayand/or day of week usage, a rate of usage (e.g., average times per monthor week), ride service type usage, special vehicle and/or driverrequests, and the like. As an example, the profile manager 150 of thetransport system 100 can analyze the ride history data 143 in each riderprofile 142 to determine the personal preferences and interests of therider 174. In addition, the profile manager 150 can analyze the ridehistory data 143 in the rider profile 142 to determine the demographicsof the rider 174, such as the rider's age, gender, income level, and thelike. The profile manager 150 may then combine the rider's demographicsand personal interests—as determined from the rider history data 143—topersonalize and improve the ride experience of the rider 174 for futurerides.

In various implementations, the profile manager 150 can analyze thecontextual usage of the on-demand transport service to classify therider's characteristics and interests. In some aspects, the profilemanager 150 can implement unsupervised data analysis (e.g., viaexecution of a set of machine learning algorithms) in order to infer therider's characteristics, such as the rider's demographics, preferences,and personal interests. As an example, a daily commuter that utilizesthe luxury ride service type of the on-demand transportation service forcommutes to and from work in normal working hours on weekdays can beclassified as a relatively affluent working professional. The drop-offlocation of the rider 174 can further indicate the profession of therider 174. For example, if the rider 174 utilizing the luxury rideservice is routinely dropped off at a hospital, the profile manager 150can classify the rider 174 as a doctor. Furthermore, the drop-off timesand specific drop-off locations around a hospital (e.g., in certainwings of the hospital, such as a cancer wing, intensive care section,emergency room entrance, family care section, pediatrics section, etc.)can indicate the type of doctor and thus the likely interests of thatdoctor when utilizing previously analyzed mass data to determine thedoctor's personal interests.

As another example, the profile manager 150 can determine—from the ridehistory data 143 of the rider 174—whether the rider 174 is likely aparent, and may further determine a relative age of the rider's child orchildren. In some examples, the profile manager 150 may further identifywhether the rider is likely a single working parent. For example, arider 174 that routinely inputs a drop-off location proximate to a daycare center or school before proceeding to a professional building, suchas a corporate headquarters, business complex, university, etc., can bereadily identified as a single working parent. As described herein, theage and gender or sex of the rider 174 can further be determined throughanalysis of the ride history data 143. As an example, if the rider 174occasionally utilizes the on-demand transportation service to go tonight club districts at nighttime hours, the profile manager 150 candetermine that the rider 174 is relatively young. Conversely, theprofile manager 150 can determine that the rider 174 is relativelyelderly if the pick-up location of an occasional rider 174 is typicallyin front of a retirement home. Drop-off locations can also be indicatorsof the rider's demographics. For example, a monthly drop off at acosmetology salon can indicate a female rider, whereas a monthlydrop-off at a barber shop can indicate a male rider. Such demographicinformation may be utilized by the personalization engine 120 topersonalize the ride experience of the rider 174. In certain aspects,the personalization engine 120 can do so by providing highly targetedadvertising to the rider 174 while the rider 174 is on-trip. Asdescribed herein, the rider's consumption of this advertising can causethe ride fare to be discounted in accordance with a discountingcalculation.

In various examples, the profile manager 150 can manage cluster logs 144by clustering the requesting users 174 based on like personal interestsand/or demographics. In some aspects, the profile manager 150 canrepresent each rider 174 as a sequence of symbols (e.g., charactersand/or numbers) or as a vector, where each symbol represents a commoncharacteristic of the rider 174 (e.g., a demographic characteristic or apersonal interest characteristic). Accordingly, the profile manager 150can classify the cluster logs 144 in terms of common characteristics inorder to more readily provide a personalized ride experience for therider 174. As an example, the cluster logs 144 can be referenced by apersonalization engine 120 of the transport system 120 in order toprovide personalized content or ride experience services to the rider174 (e.g., reading material, video content, audio content, advertisingcontent, gaming content and/or services, and the like). As such, theprofile manager 150 can attribute each rider 174 with a uniqueidentifier and/or account number, which can be utilized by thepersonalization engine 120 to reference the cluster logs 144 when therider 174 makes a pick-up request 171.

In some examples, after each ride, feedback information 177 can beprovided by the user 174, such as ratings, comments, and complaints,which can be utilized by the profile manager 150 in maintaining andupdating the requesting user's 174 rider profile 142. Accordingly, whenthe requesting user 174 submits a pick-up request 171 and is matchedwith an available driver 184 or SDV 194 by the selection engine 130, theprofile manager 150 can receive ride data 131 indicating the match,routing information from the pick-up location to the destination 172,and any ride updates or specialized requests made by the requesting user174 (e.g., a baby seat, a bike rack, roof racks, audio and/or videorequests, etc.). The profile engine 150 can utilize the ride data 131 toprovide profile updates 151 to the requesting user's 174 rider profile142, or generally include the ride data 131 with the requesting user's174 ride history data 143.

Additionally, the profile manager 150 can provide a unique identifier153 of the requesting user 174 to the personalization engine 120. Insome examples, the personalization engine 120 can utilize the uniqueidentifier 153 of the requesting user 174 to perform one or more lookups121 in the database 140 for information indicating the requesting user's174 demographics and/or personal interests and preferences. In variousexamples, the personalization engine 120 can utilize the uniqueidentifier 153 to perform lookups 121 in the cluster logs 144 toidentify the classifications of the requesting user 174. Theseclassifications can indicate the demographics and interests that therequesting user 174 has in common with other clustered users. Invariations, the personalization engine 120 can perform a lookup 121 inthe requesting user's 174 rider profile 142 to identify the rider'sdemographics and personal interest information on an individual basis.

According to examples described herein, the personalization engine 120can retrieve the demographics and/or personal interest data 146 of therequesting user 174 in order to provide a personalized ride experiencefor the user 174. In some aspects, personalization of the rideexperience can entail providing on-board services, such as videoconferencing or phoning services, content access (e.g., news or otherinformational articles or videos), and/or entertainment access (e.g.,television or movie content, gaming access, etc.). In various examples,the personalization engine 120 can utilize the demographics and/orpersonal interest data 146 as a filter to provide on such ride services,features, and content in which the requesting user 174 is most likely tobe interested. Thus, if the demographics and/or personal interest data146 indicates that the requesting user 174 would most likely have nointerest in content geared towards children, then the personalizationengine 120 can exclude and children's content from the content andservice options (e.g., video games, cartoon shows, children's storybooks, etc.).

In various implementations, the personalization engine 120 can utilizethe demographics and/or personal interest data 146 of the requestinguser 174 to target personalized content for the user 174. In suchimplementations, the personalization engine 120 can access a number ofcontent sources 195, either locally or over one or more networks 160, toselect content items 198 to provide to the user 174 while the user ison-trip to the destination 172. In some aspects, the user representationas a vector or sequence of numbers or symbols can be directly correlatedto the clusters to which the user 174 belongs. Each cluster can beindicative of a set of targeted advertisements, and can befoundationally based on the demographics and personal interests of theusers in the cluster. Accordingly, the cluster logs 144 can bereferenced by the personalization engine 120 based on the uniqueidentifier 153 of the requesting user 174. Based on the clustersassociated with the requesting user 174, the personalization engine 120can generate content calls 127 to the content sources 195 to receive aset of highly targeted content items 198 for the requesting user 174.These content items 198 can comprise advertising videos, images, text,interactive features, clickable or selectable links to additionalcontent, and the like.

According to various examples, the personalization engine 120 canperform the lookups 121 and retrieve the targeted content items 198while the selected vehicle is en route to rendezvous with the requestinguser 174 at the pick-up location. In some aspects, the content items 198can be provided to the requesting user 174 via the executing riderapplication 175 on the rider device 170 of the requesting user 174(e.g., prior to pick-up and while on-trip). Additionally oralternatively, the content items 198 can be provided to the requestinguser 174 via interior display screens within the matched vehicle (e.g.,the optimal driver's vehicle or the matched SDV 194). Thus, in additionto receiving the unique identifier 153 of the requesting user 174, thepersonalization engine 120 can further receive identifying informationof the matched vehicle in order to push or otherwise transmit thetargeted content items 198 to the matched vehicle to be displayed to therequesting user 174 upon entering the vehicle and/or while the vehicleis on-trip to the destination 172.

As provided herein, the targeted content items 198 can be directlytargeted based on the determined interests of the requesting user 174 asindicated in the user's ride history data 143. These data 143 canindicate various interests of the user 174, such as shopping or productinterests, an interest in certain sports or sport teams, musicinterests, and the like. The personalization engine 120 can utilize thedemographic and/or personal interest data 146 of the requesting user toprovide targeted content items 198 to the user 174. As described infurther detail with respect to FIG. 2 below, the personalization engine120 can further monitor consumption data 129—corresponding to the user'sinteraction with or intake of the content items 198—in order to, forexample, calculate a value of the consumed content. In some aspects,this value can be utilized by the transport system 100 as an addedrevenue source from advertisers, as a discount metric for the cost ofthe ride to the user 174, or a combination of both.

According to certain variations, the selection of the driver 184, SDV194 or vehicle type, specialized features, audio or video settings inthe vehicle, and any specialized features of the vehicle can also bemade by the selection engine 130 based on the demographics and/orpersonal interest data 146. For example, when receiving a pick-uprequest 171, the selection engine 130 can receive profile data 149 ofthe requesting user 174 from the profile manager 150 that indicates therequesting user's personal preferences or needs. For example, theprofile data 149 can indicate that the requesting user 174 has adisability and requires that the matched vehicle have certainspecialized features, such as a wheelchair lift. As another example, theprofile data 149 can indicate that the requesting user 174 usuallyrequires a bike rack or a baby seat. In such examples, without receivinga definitive request for such features, the selection engine 130 canprioritize vehicles that include these specialized features when makingan optimal selection.

In further examples, the profile data 149 and/or the demographics andpersonal interest data 146 can indicate route preferences of therequesting user 174. For example, over time, the transport system 100can collect data indicating that the rides consumed by the user 174 haveresulted in detours or alternative routes, which can indicate that theuser prefers to avoid certain traffic areas or neighborhoods. Invariations, these detours or alternatives can indicate that the user 174prefers scenic routes as opposed to more centralized routes throughdreary cityscapes or crowded freeways. In any case, the profile manager150 can identify, in the ride history data 143, the user's preferencesin relation to route characteristics. In additional to submitting atransport invitation 132 to a matched driver 184 or SDV 194, theselection engine 130 can further provide a personalized route 133 forthe requesting user 174 indicating a preferred route to the destination172 as opposed to the most temporally or spatially optimized route. Insuch examples, the selection engine 130 may provide a confirmationscreen 134 to the requesting user 174 indicating that the personalizedroute 133 will be taken, and can enable the user 174 to accept or rejectthe route option.

Further description of the personalization engine 120 and other featuresof the transport system 100 is provided below with respect to FIG. 2.

FIG. 2 is a block diagram illustrating an example ride experiencepersonalization system, according to examples described herein. Variousexamples of the ride experience personalization system 200 of FIG. 2 cancomprise one or more components and include functionality described inconnection with the personalization engine 120 of the transport system100 of FIG. 1. Accordingly, the ride experience personalization system200 as shown and described with respect to FIG. 2 may be included as acomponent of the backend transport system 100 of FIG. 1, or may be aseparate and independent component or service with respect to thetransport system 100. Accordingly, in certain implementations, the rideexperience personalization system 200 shown in FIG. 2 may be in directcommunications with certain components and logical blocks as shown anddescribed with respect to FIG. 1. Furthermore, in the below descriptionof FIG. 2, reference may be made to reference characters representinglike features as shown and described with respect to FIG. 1.

Referring to FIG. 2, the ride experience personalization system 200 caninclude a database 240 comprising content logs 246 that include variouscontent items 296 from content sources 295. For example, the businessentity corresponding to the transport system 100 may be partnered withcertain advertisers or advertising intermediaries to provide targetedcontent to users 174 of the on-demand transportation service. In formingsuch relationships, the content sources 295 can produce the contentitems 296 and can provide the content items 296 to a content compiler235 of the ride experience personalization system 200. The contentcompiler 235 can classify each content item 296 for certain targetaudiences in accordance with various targeting parameters known in thepertinent art.

In certain implementations, the content compiler 235 receive keywords orother content classifiers with the individual content items 296 from thecontent courses 295, and can store the content items 296 in content logs246 in the database 240. As provided herein, the content logs 246 can becorrelated to rider clusters 244 comprised of targeted riders 174 withcommon or similar personal interests and/or demographic characteristics.Each rider 174 may be associated with any number of rider clusters 244based on the determined demographics and personal interests of the rider174. Each rider cluster 244 can be associated with targeting criteriawhich a content engine 250 of the ride experience personalization system200 can utilize to provide targeted, personalized content 232 toindividual riders 174 based on the rider clusters 244 to which the rider174 belongs.

According to examples, when the rider 174 transmits a pick-up request171 to the transport system 100, a content interface 230 of the rideexperience personalization system 200 can receive a unique identifier287 of the requesting rider 174. For example, the unique identifier 287may be received from a rider interface 125 of the transport system 100,and/or may be included with the pick-up request 171. As provided herein,the unique identifier 287 can be utilized by the content engine 250 toreference the rider clusters 244 to which the requesting rider 174belongs. In some aspects, the unique identifier 287 can comprise avector or character sequence that directly indicates the classificationsof the rider 174, or the rider clusters 244 to which the rider 174belongs. Accordingly, the content engine 250 can perform lookups 251 inthe content logs 246 for targeted content items 296 based on the riderclusters 244 associated with the requesting rider 174.

In some examples, the content engine 250 can retrieve the personalizedcontent 232 from the content logs 246, and cause the personalizedcontent 232 to be presented to the rider 174 on a display the riderdevice 285 (e.g., via the executing rider application 175 and over thenetwork(s) 260). Additionally or alternatively, the content engine 250can provide the personalized content 232 to the rider device 285 whenthe rider application 175 is in an “on-trip” mode 289, corresponding tothe rider 174 being transported by the matched driver 184 or SDV 194from the pick-up location to the destination 172. Thus, an indication ofthe on-trip mode 289 of the rider device 285 can trigger the contentengine 250 to begin providing the personalized content 232. Invariations, the content engine 250 can also provide the personalizedcontent 232 to the rider device 285 while the rider 174 is awaitingpick-up, or when the matched vehicle is en route to the pick-up locationto rendezvous with the rider 174. In still further variations, thecontent engine 250 can provide the content to the rider device 285whenever the rider application 175 is executing. In such variations, thepersonalized content 232 may be displayed on the display screen of therider device 285 in discreet areas or as native advertisements withinthe functional content of the rider application 175.

The foregoing display methods and triggers described with respect to therider device 285 may alternatively be implemented on a vehicle displaysystem 290 of the matched vehicle. Accordingly, the content engine 250can provide the personalized content 232 to the matched vehicle over theone or more networks 260 to be displayed on the vehicle display system290 while the matched vehicle transports the rider 174 to thedestination 172. In one example, the on-trip status 289 of the riderapplication 175 can trigger the content engine 250 to push or otherwisetransmit content data to the vehicle display system 290 of the matchedvehicle, which can cause the personalized content 232 to be displayedwhen the rider 174 enters the vehicle, and while the rider 174 is beingtransported. The content engine 250 can receive identifying informationof the matched vehicle, correlate the unique identifier 287 of the rider174 with the matched vehicle, retrieve the personalized content 232 forthe rider 174 from the content logs 246, and transmit the personalizedcontent 232 to the matched vehicle to be displayed on the vehicle'sdisplay system 290 for the rider 174.

As described herein, the personalized content 232 can comprise anycontent specifically selected for the rider 174 based at least in parton the ride history data 143 of the rider 174. In furtherimplementations, the personalized content 232 can be further selectedbased on third party data, such as the rider's content browsing historyon a search engine, or direct interest data from the rider'sinteractivity with certain social media platforms (e.g., “like”information from the rider's FACEBOOK profile). Thus, the rider 174 canfurther be associated with certain rider clusters 244 based on acombination of the rider's ride history data 143 and third partyinformation that further indicates the rider's personal interests,preferences, and demographics.

The personalized content 232 can include interactive content, such asinteractive gaming applications, scrollable articles, video content(e.g., a news or entertainment program), or widgets linking the riderapplication 175 to third party applications of partnered entities (e.g.,a social media application or content browsing application). Thepersonalized content 232 may also include targeted advertising content,which can be interactive, passively viewed, clicked or selected topresent additional content, and the like.

In certain examples, the ride experience personalization system 200 caninclude a content monitor 220 that can receive perception data 286and/or interaction data 288 corresponding to the rider's viewing and/orinteraction with the personalized content 232. In some aspects, thecontent monitor 220 can track the rider's advertising consumption overthe course of a ride. Additionally or alternatively, the content monitor220 can track the rider's advertising consumption whenever the riderapplication 175 is executing on the rider device 285. Accordingly, theperception data 286 and interaction data 288 can be monitored by thecontent monitor 220 over the one or more networks 260, and can also bequantified by the content monitor 220. For example, the content monitor220 can determine an overall time that certain advertising content wasdisplayed to the user (e.g., either on the rider device 285, the vehicledisplay system 290, or both). Additionally or alternatively, the contentmonitor 220 can determine a level of interaction with the advertisingcontent (e.g., a number of clicks or selections on ads, videoadvertisements selectively viewed, etc.).

According to various implementations, the ride experiencepersonalization system 200 can include a ride discount calculator 210,which can receive an ad consumption report 222 from the content monitor220. The ad consumption report 222 can be based on the perception data286 and the interaction data 288, and can indicate the level ofinteraction with the personalized content 232 (e.g., targetedadvertising content) by the rider 174. In one example, the adconsumption report 222 can include only ad consumption data by the rider174 corresponding to a single trip in which the rider 174 utilizes theon-demand transportation service. In this example, the ride discountcalculator 210 can calculate a value of the consumed content based onthe ad consumption report 222, and provide a discount 212 to the rideraccount 248 of the rider 174 based on the calculated value.

In variations, the ride discount calculator 210 can receive adconsumption reports 222 from the content monitor 220 that indicate ageneral total level of ad consumption by the rider 174 (e.g., wheneverthe rider application 175 is executing on the rider device 285). Theride discount calculator 210 can calculate a total value of the overallad consumption by the rider 174, and provide a discount 212 to the rideraccount 248 of the rider 174 for any future on-demand trips based on thevaluation of the consumed ads. According to examples, the ride discountcalculator 210 can calculate value of ad consumption reports 222 inaccordance with known valuation parameters. Furthermore, the rideexperience personalization system 200 can provide and monitor usage ofpersonalized content 232 for every user 174 of the on-demand transportservice in a given region.

Rider Device

FIG. 3 is a block diagram illustrating an example rider device executinga designated rider application for a transport arrangement service, asdescribed herein. In many implementations, the rider device 300 cancomprise a mobile computing device, such as a smartphone, tabletcomputer, laptop computer, VR or AR headset device, and the like. Assuch, the rider device 300 can include typical telephony features suchas a microphone 345, a camera 350, and a communication interface 310 tocommunicate with external entities using any type of wirelesscommunication protocol. In certain aspects, the rider device 300 canstore a designated application (e.g., a rider app 332) in a local memory330.

In response to a user input 318, the rider app 332 can be executed byone or more processors 340, which can cause an app interface 342 to begenerated on a display screen 320 of the rider device 300. The appinterface 342 can enable the user to, for example, check current pricelevels and availability for various ride service types of the on-demandtransportation arrangement service. In various implementations, the appinterface 342 can further enable the user to view informationcorresponding to the multiple ride service types, and select from themultiple ride service types, such as a carpooling service type, aregular ride-sharing service type, a professional ride service type, avan transport service type, a luxurious ride service type, aself-driving vehicle service type, and the like. Example services thatmay be browsed and requested can comprise those services provided byUBER Technologies, Inc. of San Francisco, Calif.

The user can generate a pick-up request 367 via user inputs 318 providedon the app interface 342. According to examples described herein, theuser can provide user inputs 318 on the app interface 342, which can beprocessed by the processor(s) 340 and/or the transport system 390 overthe network(s) 380 to provide personalized content 396. In doing so, therider application 332 can enable a communication link with a transportsystem 390 over the network 380, such as the transport system 100 asshown and described with respect to FIG. 1. To request transportation,the user can input a destination and/or pick-up location, select a rideservice type, configure the ride, and/or make one or more specializedrequests. Furthermore, the app interface 342 can provide upfrontinformation about each available ride service type, such as an estimatedtime of arrival at the pick-up location or destination, or an upfrontcost for the ride

Once the ride service type is selected and the user wishes to request aride, the processor(s) 340 can transmit the pick-up request 367 via thecommunications interface 310 to the backend transport facilitationsystem 390 over a network 380. In response, the rider device 300 canreceive a confirmation 369 from the transport facilitation system 390indicating the selected driver and vehicle that will service the pick-uprequest 367 and rendezvous with the user at the pick-up location. Invarious examples, the rider device 300 can further include a GPS module360, which can provide location data 362 indicating the current locationof the requesting user to the transport system 390 to, for example,establish the pick-up location and/or select an optimal driver orautonomous vehicle to service the pick-up request 367.

According to examples described herein, the transport system 390 canprovide personalized content 396 to the rider device 300 over thenetwork 380. The personalized content 396 can comprise the content items198 or personalized content 232 as shown and described with respect toFIGS. 1 and 2 respectively. In some aspects, the rider device 300 cantransmit input data 328—corresponding to the user inputs 318 on the appinterface 342—to the transport facilitation system 390 that indicates alevel of interaction by the rider with the personalized content 396. Theinput data 328 can be processed by the transport facilitation system 390(e.g., the personalization engine 120 of FIG. 1, or the ride discountcalculator 210 of FIG. 2) to determine a value of the overallconsumption of the personalized content 396 by the rider (e.g., todiscount an individual ride).

As provided herein, one or more processes described in connection withthe transport system 100 of FIG. 1 can be performed by the processor(s)340 of the rider device 300 executing the rider application 332. Forexample, certain content updates for the app interface 342 can begenerated by the processor(s) 340 as opposed to the content engine 250as shown and described with respect to FIG. 1. Accordingly, the contentupdates corresponding to certain rider application 332 screens andinterface can be generated by the processor(s) 340 of the rider device300 via execution of the rider application 332 or through backendprocesses by the transport facilitation system 100 implementing the rideexperience personalization system 200.

Methodology

FIG. 4 is a flow chart describing an example method of personalizingride experience based on contextual ride usage data, according toexamples described herein. In the below description of FIG. 4, referencemay be made to reference characters representing like features as shownand described with respect to FIGS. 1 through 3. Furthermore, the methoddescribed in connection with FIG. 4 may be performed by an exampletransport system 100 implementing a ride experience personalizationsystem 200 as shown and described with respect to FIGS. 1 and 2.Referring to FIG. 4, the transport system 100 can manage an on-demandtransportation service that connects requesting users 174 with availabledrivers 184 and/or SDVs 194 (400). Over time, the transport system 100can compile ride history data 143 for the requesting users 174 (405).The ride history data 143 can comprise contextual usage datacorresponding to the user's usage of the on-demand transportationservice (407). In some examples, the contextual usage data can comprisetime of day and day of week usage of the transportation service. Thecontextual usage data can further include the types of ride servicesrequested by the user 174, any specialized requests made by the user 174(e.g., a baby seat), route detour requests, feedback data 177corresponding to user ratings of the ride experience, and the like. Thecontextual usage data can further comprise pickup and drop-off locationsof the user 174 (409), which can indicate certain interests,demographics, and behavior patterns of the user 174.

In various examples, the transport system 100 can determine demographicand personal interest information 146 of the user 174 (410). In someaspects, this information 146 can include the user's product interests(412), hobbies (413), and profession (414). For example, morning weekdaydrop-off locations can provide indicators of the user's profession,whereas weekend drop-off locations may indicate the user's interests orhobbies. The transport system 100 can receive ride requests or pick-uprequests 171 from the users 174 (415). In response, the transport system100 can select a most optimal driver or SDV to service the request 171,and transmit a transport invitation 132 to the selected availablevehicle to service the request 171 (420). Thereafter, the transportsystem 100 can generally personalize one or more ride characteristicsbased on the determined demographics and personal interest information146 as determined from the user's ride history (425). For example, thetransport system 100 can transmit personalized route data 133 to theselected vehicle to personalize the route for the user 174 (426).Additionally or alternatively, the transport system 100 can providepersonalized content 232 to the rider 174 (e.g., via the rider'scomputing device 170 or the vehicle display system 290 of the selectedvehicle) (427).

FIG. 5 is a lower level flow chart describing an example method ofpersonalizing ride experience based on contextual ride usage data,according to examples described herein. In the below description of FIG.5, reference may also be made to reference characters representing likefeatures as shown and described with respect to FIGS. 1 through 3.Furthermore, the method described in connection with FIG. 5 may beperformed by the transport system 100 implementing the ride experiencepersonalization engine 200 as shown and described with respect to FIGS.1 and 2. Referring to FIG. 5, as described herein, the transport system100 can manage an on-demand transportation service that connectsrequesting users 174 with available drivers 184 and/or SDV 194 (500). Indoing so, the transport system 100 can receive pick-up requests 171 fromthe requesting users 174 (502), and select optimal vehicles (eitherhuman driven or autonomous) to service the pick-up requests 171 (504).

In certain implementations, the transport system 100 can implement theon-demand transportation platform by providing a rider application 175to the users 174, where the rider application 175 is executable on therider's computing device to enable the user 174 to request on-demandrides (505). The transport system 100 may also manage rider profiles 142comprising ride history data 143 indicating the contextual usage of theon-demand transportation service by each individual user (510). Forexample, the profile manager 150 of the transport system 100 can updatethe rider profiles 142 based on ride data 131 for each ride consumed bythe user 174 (512). The ride data 131 can indicate any details of anindividual ride, such as the time and date of pick-up and drop-off, thepick-up and drop-off locations, and the requested ride service type. Incertain implementations, the profile manager 150 can further update therider profiles 142 based on feedback data 177 provided to the transportsystem 100 (e.g., ratings data for any particular ride).

In some examples, the transport system 100 can determine certaincharacteristics of the user 174 based on accelerometer data from anaccelerometer or an inertial measurement unit (IMU) of the user'scomputing device 170. For example, estimates of the user's height andweight may be determined from accelerometer data of the user's computingdevice 170. Additionally, any injuries or physical disabilities may alsobe determined based on the accelerometer data. According to someexamples, the transport system 100 can monitor accelerometer data fromthe user device 170 of the user 174 to construct a gait profile for theuser 174 (515). For example, execution of the rider application 175 bythe rider device 170 can provide network access to the IMU oraccelerometer of the rider device 170 to monitor and analyze theaccelerometer data. The gait profile can be analyzed by the transportsystem 100 to determine certain user characteristics, such as height andweight. Along these lines, certain demographic information may also beextrapolated from the gait profile of the user 174.

Accordingly, the transport system 100 can determine demographic andpersonal interest information 146 for each user 174 of thetransportation service (520). As provided herein, this information 146can be determined by the transport system 100 by analyzing the ridehistory data 143 of the user 174. As mentioned above, the demographicand personal interest data 146 can also be determined from the gaitprofile of the user 174, and/or third party data from third partysources (e.g., indicating the user's content browsing history, socialmedia activity, and the like). In some aspects, the transport system 100can cluster the users 174 based on the personal interests anddemographic data 146 as determined, at least in part, by the ridehistory of the users 174 (525).

In some implementations, the transport system 100 can receive andclassify content items 198 from any number of content sources 195 (530).The transport system 100 can associate the content items 198 with theclusters for content targeting (535). As described herein, some or allof the content items 198 can comprise advertising content from partneredadvertisers or advertising suppliers or intermediaries.

In certain aspects, receiving the pick-up request 171 can trigger thetransport system 100 to perform lookups 251 for personalized content 232to provide to the user 174. In variations, each time the riderapplication 175 is executed on the rider device 170, the transportsystem 100 can provide personalized content 232 to the rider device 170.As such the timing and triggers of providing the personalized content232 to the rider device 170 can vary.

In one aspect, the transport system 100 can personalize the rideselection based on the rider's personal interest and demographic data146 (540). For example, in response to receiving the pick-up request 171from a requesting user 174, the transport system 100 can determine apreferred ride service type for the user 174 and automatically selectthe preferred service type for the user 174 (542). In further examples,the transport system 100 can determine a preferred vehicle type (e.g., acompact, midsize, or full size car, minivan, van, SUV, electric vehicle,hybrid vehicle, luxury vehicle, self-driving vehicle, specific vehiclebrands and models, newer vehicles versus older vehicles, and the like),and can automatically select a preferred vehicle type for the requestinguser 174 (544). Such determinations of ride service type or vehicle typemay be based on the compiled ride data 131 for the user 174, or certaincharacteristics of the user 174, such as determinations of whether theuser 174 is a parent, a male, female, elderly, young, affluent,disabled, fashionable, apathetic, oblivious, etc.

In certain aspects, the transport system 100 can further personalize theroute based on the personal interests and demographics data 146 of theuser 174 (545). For example, the ride history data 143 can indicate thatthe user 174 prefers certain routes, such as more scenic routes, orroutes that are less risky (e.g., avoiding congested inner cities ordangerous highways). Accordingly, in some aspects, the transport system100 can automatically select a more scenic route for the user 174, andtransmit the route data to the driver device 180 of the selected driver184, or to a communication interface of a selected SDV 194 (547).Additionally or alternatively, the transport system 100 can select aless risky route or safest route option among a plurality of routeoptions, and transmit route data to the selected vehicle accordingly(549).

Accordingly to various examples, the transport system 100 can furtherprovide a selectable feature on the user interface 152 of the riderapplication 175 that enables the user 174 to selectively view orinteract with personalized content 232 (550). In one example, theselectable feature can indicate and offer that viewing and/orinteracting with the personalized content 232 can result in a discountedride (554). In variations, the selectable feature can be provided on oneor more display screens of the selected vehicle upon rendezvousing withthe user 174. In certain implementations, the selectable feature can bedisplayed to the user 174 (either via the rider device 175 or displaysystem of the vehicle) based on the rider 174 have an on-trip status(552).

In various aspects, the transport system 100 can provide thepersonalized content 232 to the rider 174 (555). As described herein,the personalized content 232 can be displayed on a display screen of therider device 170 (557), or the vehicle display system 290 of theselected vehicle (559). In some examples, the transport system 100 canalso monitor user perception and/or interaction data 286, 288corresponding to the user's viewing and/or interaction with thepersonalized content 232 (560). Based on the perception and/orinteraction data 286, 288, the transport system 100 may then calculatean overall value of the rider's consumption of the personalized content232 (e.g., targeted advertising content), and provide a discount for theride based on the calculated value (565).

Hardware Diagram

FIG. 6 is a block diagram that illustrates a computer system upon whichexamples described herein may be implemented. A computer system 600 canbe implemented on, for example, a server or combination of servers. Forexample, the computer system 600 may be implemented as part of a networkservice for providing transportation services. In the context of FIGS. 1and 2, the transport system 100 and ride personalization system 200 maybe implemented using a computer system 600 such as described by FIG. 6.The transport system 100 and ride personalization system 200 may also beimplemented using a combination of multiple computer systems asdescribed in connection with FIG. 6.

In one implementation, the computer system 600 includes processingresources 610, a main memory 620, a read-only memory (ROM) 630, astorage device 640, and a communication interface 650. The computersystem 600 includes at least one processor 610 for processinginformation stored in the main memory 620, such as provided by a randomaccess memory (RAM) or other dynamic storage device, for storinginformation and instructions which are executable by the processor 610.The main memory 620 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by the processor 610. The computer system 600 may also includethe ROM 630 or other static storage device for storing staticinformation and instructions for the processor 610. A storage device640, such as a magnetic disk or optical disk, is provided for storinginformation and instructions.

The communication interface 650 enables the computer system 600 tocommunicate with one or more networks 680 (e.g., cellular network)through use of the network link (wireless or wired). Using the networklink, the computer system 600 can communicate with one or more computingdevices, one or more servers, and/or one or more self-driving vehicles.In accordance with examples, the computer system 600 receives pick-uprequests 682 from mobile computing devices of individual users. Theexecutable instructions stored in the memory 630 can include selectioninstructions 622, which the processor 610 executes to select an optimaldriver or SDV to service the pick-up request 682. In doing so, thecomputer system can receive vehicle locations 684 of drivers and SDVsoperating throughout the given region, and the processor can execute theselection instructions 622 to select an optimal driver or SDV from a setof available vehicles, and transmit a transport invitation 652 to enablethe driver to accept or decline the ride service offer, or to instructthe matched SDV to rendezvous with the requesting user.

The executable instructions stored in the memory 620 can also includedata analysis instructions 624, which enable the computer system 600 toanalyze the ride history data of each user to determine the demographicsand/or personal interests of the user. For example, the computer system600 can compile ride history data in stored rider profiles 628 of theuser, which the computer system 600 can analyze via execution of thedata analysis instructions 624 or a machine learning model to determinethe demographics and/or personal interests of the user. Accordingly,execution of the data analysis instructions 624 can further cause theprocessors 610 to cluster the users in cluster logs 626 that canclassify the user based on interests, demographics, preferences, or anyother clustering metric for content personalization. The executableinstructions can further include content instructions 625, which, whenexecuted by the processor 610, cause the computer system 600 to providepersonalized content 654 to the user over the network 680.

By way of example, the instructions and data stored in the memory 620can be executed by the processor 610 to implement an example transportsystem 100 of FIG. 1 and/or the ride personalization system 200 of FIG.2. In performing the operations, the processor 610 can receive pick-uprequests 682 and driver locations 684, and submit transport invitations652 to facilitate the servicing of the requests 682. Furthermore, theprocessor 610 can determine the demographics and interests of the userin order to provide personalized content 654, such as targetedadvertising content, to the user (e.g., via the display screen of theuser device or a display system of the matched vehicle).

The processor 610 is configured with software and/or other logic toperform one or more processes, steps and other functions described withimplementations, such as described with respect to FIGS. 1-5, andelsewhere in the present application. Examples described herein arerelated to the use of the computer system 600 for implementing thetechniques described herein. According to one example, those techniquesare performed by the computer system 600 in response to the processor610 executing one or more sequences of one or more instructionscontained in the main memory 620. Such instructions may be read into themain memory 620 from another machine-readable medium, such as thestorage device 640. Execution of the sequences of instructions containedin the main memory 620 causes the processor 610 to perform the processsteps described herein. In alternative implementations, hard-wiredcircuitry may be used in place of or in combination with softwareinstructions to implement examples described herein. Thus, the examplesdescribed are not limited to any specific combination of hardwarecircuitry and software.

It is contemplated for examples described herein to extend to individualelements and concepts described herein, independently of other concepts,ideas or systems, as well as for examples to include combinations ofelements recited anywhere in this application. Although examples aredescribed in detail herein with reference to the accompanying drawings,it is to be understood that the concepts are not limited to thoseprecise examples. As such, many modifications and variations will beapparent to practitioners skilled in this art. Accordingly, it isintended that the scope of the concepts be defined by the followingclaims and their equivalents. Furthermore, it is contemplated that aparticular feature described either individually or as part of anexample can be combined with other individually described features, orparts of other examples, even if the other features and examples make nomention of the particular feature. Thus, the absence of describingcombinations should not preclude claiming rights to such combinations.

1.-20. (canceled)
 21. A transport system comprising: one or morecomputing devices configured to: compile ride history data for eachrespective user of a plurality of users of an on-demand transportationservice, the ride history data comprising contextual usage of theon-demand transportation service by the respective user; receive apick-up request from a user of the plurality of users; transmit, basedon the pick-up request, a transport request to a vehicle to provide atransportation service for the user; determine, based on the compiledride history data, personalized content for the user; transmit thepersonalized content for display on a display screen of the vehicle oron a display screen of a user device associated with the user; receivecontent data associated with the personalized content displayed duringthe transportation service; and determine, based on the content data, areward for the user.
 22. The transport system of claim 1, wherein theone or more computing devices are further configured to: provide aselectable feature on the at least one of a display screen of thevehicle or a display screen of the user device, the selectable featureenabling the respective user to select from (i) viewing the personalizedcontent to receive the reward, or (ii) provide the ride with nopersonalized content.
 23. The transport system of claim 1, whereindetermining, based on the compiled ride history, personalized contentfor the user further comprises: determining demographic and personalinterest information of the user based on the compiled ride history; andselecting personalized content that matches the demographic and personalinterest information of the user.
 24. The transport system of claim 1,wherein the content data associated with the personalized contentdisplayed during the transportation service indicates an amount ofpersonalized data displayed on the display screen of the vehicle or onthe display screen of the user device associated with the user.
 25. Thetransport system of claim 4, wherein the reward is determined based, atleast partially, on the amount of personalized data displayed on thedisplay screen of the vehicle or on the display screen of the userdevice associated with the user.
 26. The transport system of claim 1,wherein the content data associated with the personalized contentdisplayed during the transportation service indicates a monitored levelof consumption or user interaction with the personalized data displayedon the display screen of the vehicle or on the display screen of theuser device associated with the user.
 27. The transport system of claim6, wherein the reward is determined based, at least partially, on themonitored level of consumption or user interaction with the personalizeddata displayed on the display screen of the vehicle or on the displayscreen of the user device associated with the user.
 28. The transportsystem of claim 1, wherein the one or more computing devices are furtherconfigured to: identify a pick-up location and a destination from thepick-up request; wherein the one or more computing devices areconfigured to personalize a route between the pick-up location and thedestination.
 29. The transport system of claim 3, wherein the ridehistory data indicates drop-off locations of the respective user, andwherein the one or more computing devices are further configured todetermine the demographic and personal interest information of therespective user based, at least in part, on the drop-off locations. 30.The transport system of claim 9, wherein the demographic informationcomprises at least an estimated age and sex of the respective user. 31.The transport system of claim 8, wherein an executed machine learningmodel further causes the one or more computing devices to: analyze theride history data for the users of the on-demand transportation servicein order to cluster the users of the on-demand transportation servicebased on at least one of common demographics or common personalinterests; and for each specified cluster, establish a set of parametersthat provides each user in the specified cluster with ride experiencecharacteristics specific to the specified cluster.
 32. The transportsystem of claim 11, wherein the set of parameters specify content to bedisplayed to users in the specified cluster.
 33. The transport system ofclaim 11, wherein the set of parameters specify one or more vehicletypes to be selected for the users in the specified cluster.
 34. Thetransport system of claim 13, wherein the one or more vehicle typescomprise at least one of a standard vehicle, a luxury vehicle, a highcapacity vehicle, a sport utility vehicle, a specific vehicle brand, ora self-driving vehicle.
 35. The transport system of claim 11, whereinthe set of parameters specify one or more ride service types to beselected for the users in the specified cluster.
 36. The transportsystem of claim 15, wherein the one or more ride service types compriseone or more of a carpooling service, a standard ride-sharing service, aprofessional driver service, a black car service, a luxury ride service,a high capacity vehicle service, or a self-driving vehicle service. 37.The transport system of claim 11, wherein the machine learning modelfurther causes the one or more processors to: classify the respectiveuser into one or more clusters; wherein the machine learning modelcauses the one or more processors to personalize the one or more ridecharacteristics of the ride based on the classification of therespective user in each of the one or more clusters.
 38. The transportsystem of claim 11, wherein the executed machine learning model clustersthe users, based on at least one of common demographics or commonpersonal interests, by representing each of the users as a vectorcomprising a series of values indicative of each cluster in which theuser is classified.
 39. A computer-implemented method of personalizingride experience, the method being performed by one or more processorsand comprising: compiling, by the one or more processors, ride historydata for each respective user of a plurality of users of an on-demandtransportation service, the ride history data comprising contextualusage of the on-demand transportation service by the respective user;receiving, by the one or more processors, a pick-up request from a userof the plurality of users; transmitting, by the one or more processorsand based on the pick-up request, a transport request to a vehicle toprovide a transportation service for the user; determining, by the oneor more processors and based on the compiled ride history data,personalized content for the user; transmitting, by the one or moreprocessors, the personalized content for display on a display screen ofthe vehicle or on a display screen of a user device associated with theuser; receiving, by the one or more processors, content data associatedwith the personalized content displayed during the transportationservice; and determining, by the one or more processors and based on thecontent data, a reward for the user.
 40. A non-transitorycomputer-readable-medium storing instructions that, when executed by oneor more processors, cause the one or more processors to: compile ridehistory data for each respective user of a plurality of users of anon-demand transportation service, the ride history data comprisingcontextual usage of the on-demand transportation service by therespective user; receive a pick-up request from a user of the pluralityof users; transmit, based on the pick-up request, a transport request toa vehicle to provide a transportation service for the user; determine,based on the compiled ride history data, personalized content for theuser; transmit the personalized content for display on a display screenof the vehicle or on a display screen of a user device associated withthe user; receive content data associated with the personalized contentdisplayed during the transportation service; and determine, based on thecontent data, a reward for the user.